{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b04bd5d-1dda-4f24-9e36-e9f827dbc23a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spatial级别的蒸馏"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "adc1f963-27a9-4a24-abd4-2116e228d850",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "YOLOv5 🚀 7330b3e4 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setup complete ✅ (112 CPUs, 503.5 GB RAM, 28.0/30.0 GB disk)\n"
     ]
    }
   ],
   "source": [
    "import comet_ml\n",
    "import torch\n",
    "import utils\n",
    "\n",
    "comet_ml.init(project_name='exp_100epoch')\n",
    "# 这里应该会包含100epoch的0,0.6,1.2加雾以及各个以100epoch为单位的增量\n",
    "display = utils.notebook_init()  # checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5f93a8f8-ee27-4fef-9bb6-6a74b1a0045a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_PODNet: \u001b[0mweights=./runs/train/fog_02/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTI.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=POD_layers_1_3_5_7_9_13_17_20_23, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=[], Lwf_temperature=1.0, PODNet_enable=True, Distillation_layers=[1, 3, 5, 7, 9, 13, 17, 20, 23], POD_lambda=100.0, Old_models=['./runs/train/fog_02/weights/last.pt'], DER_enable=False, DER_old_model=[]\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2895 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 7330b3e4 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/0e717873d11c4093abde69e0f390b647\u001b[0m\n",
      "\n",
      "extractors长度： 0\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo_PODNet.Detect               [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/fog_02/weights/last.pt\n",
      "Overriding model.yaml nc=26 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo_PODNet.Detect               [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007.cache... 16551 im\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m3.99 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/POD_layers_1_3_5_7_9_13_17_20_23/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/POD_layers_1_3_5_7_9_13_17_20_23\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      4.33G    0.06796     0.0442    0.06526         36        640: 1\n",
      "tensor([1.13790], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00135, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.383      0.128     0.0989     0.0489\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49       6.9G    0.05007     0.0371    0.04592         58        640: 1\n",
      "tensor([1.40107], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00207, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.387       0.39       0.33      0.166\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49       6.9G    0.04762    0.03604     0.0327         37        640: 1\n",
      "tensor([1.00429], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00277, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.486      0.497      0.479      0.249\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49       6.9G    0.04578    0.03604    0.02821         46        640: 1\n",
      "tensor([1.03451], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00282, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.579       0.54      0.556      0.294\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49       6.9G    0.04444    0.03553     0.0254         39        640: 1\n",
      "tensor([1.11317], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00308, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.596      0.562      0.584       0.32\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49       6.9G     0.0428    0.03496    0.02306         28        640: 1\n",
      "tensor([0.94534], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00332, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.613      0.584      0.607      0.342\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49       6.9G    0.04194    0.03471    0.02165         39        640: 1\n",
      "tensor([0.99599], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00303, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.628      0.584      0.614      0.352\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49       6.9G    0.04107    0.03423    0.02093         34        640: 1\n",
      "tensor([0.93868], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00308, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.648      0.637      0.664      0.385\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49       6.9G    0.04042    0.03385    0.02011         41        640: 1\n",
      "tensor([0.88747], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00285, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.675      0.615       0.67      0.394\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49       6.9G    0.03967     0.0329    0.01905         46        640: 1\n",
      "tensor([0.89033], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00293, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.701      0.638      0.691      0.413\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49       6.9G    0.03924    0.03319    0.01832         30        640: 1\n",
      "tensor([0.91006], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00284, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032        0.7      0.646      0.695      0.419\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49       6.9G    0.03858    0.03323    0.01773         26        640: 1\n",
      "tensor([0.81702], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00284, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.691      0.651       0.69       0.42\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49       6.9G    0.03816    0.03283    0.01696         33        640: 1\n",
      "tensor([0.96791], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00280, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.692      0.663      0.702      0.434\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49       6.9G    0.03786    0.03263    0.01646         30        640: 1\n",
      "tensor([0.83352], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00274, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.689      0.663      0.711      0.439\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49       6.9G    0.03768    0.03233    0.01608         33        640: 1\n",
      "tensor([0.86086], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00258, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.713      0.669      0.723      0.448\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49       6.9G    0.03701    0.03218    0.01562         23        640: 1\n",
      "tensor([0.70928], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00259, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.695      0.669      0.718       0.45\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49       6.9G    0.03671    0.03174    0.01537         40        640: 1\n",
      "tensor([0.91858], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00272, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.723      0.678      0.734      0.463\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49       6.9G    0.03632    0.03174    0.01505         45        640: 1\n",
      "tensor([0.77315], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00264, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.723      0.682      0.732      0.463\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49       6.9G    0.03614     0.0317    0.01455         20        640: 1\n",
      "tensor([0.62154], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00266, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.703      0.697      0.746      0.472\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49       6.9G    0.03576    0.03154    0.01409         38        640: 1\n",
      "tensor([0.84160], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00264, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.722      0.704      0.746      0.478\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49       6.9G    0.03552    0.03099    0.01355         33        640: 1\n",
      "tensor([0.93755], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00257, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.719        0.7       0.75      0.483\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49       6.9G    0.03528    0.03102    0.01348         28        640: 1\n",
      "tensor([0.70323], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00260, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.722      0.709      0.753      0.485\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49       6.9G    0.03491    0.03103    0.01295         27        640: 1\n",
      "tensor([0.77915], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00290, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.733      0.698      0.758       0.49\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49       6.9G     0.0347    0.03038    0.01295         29        640: 1\n",
      "tensor([1.01000], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00280, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.743      0.704      0.763      0.494\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49       6.9G    0.03459    0.03052     0.0126         35        640: 1\n",
      "tensor([0.69034], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00244, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.738      0.712      0.768      0.503\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49       6.9G    0.03423    0.03029     0.0127         31        640: 1\n",
      "tensor([0.63337], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00247, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.751      0.713      0.772      0.506\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49       6.9G    0.03411    0.03016    0.01249         40        640: 1\n",
      "tensor([0.93744], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00246, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.738      0.726      0.771      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49       6.9G    0.03384    0.03035    0.01174         29        640: 1\n",
      "tensor([0.69196], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00225, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.749      0.728      0.775      0.514\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49       6.9G    0.03363    0.02986    0.01157         26        640: 1\n",
      "tensor([0.63370], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00232, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.762      0.717      0.775      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49       6.9G    0.03335    0.03001    0.01151         64        640:  \u001b[1;38;5;196mCOMET ERROR:\u001b[0m Due to connectivity issues, there's an error in processing the heartbeat. The experiment's status updates might be inaccurate until the connection issues are resolved.\n",
      "      29/49       6.9G    0.03335    0.03001    0.01147         45        640: 1\n",
      "tensor([0.86579], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00241, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.751      0.729      0.778      0.516\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49       6.9G    0.03304    0.02951    0.01114         36        640: 1\n",
      "tensor([0.72681], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00245, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.762      0.724       0.78       0.52\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49       6.9G    0.03265    0.02904     0.0109         20        640: 1\n",
      "tensor([0.76989], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00234, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.753      0.732       0.78      0.519\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49       6.9G    0.03242    0.02905    0.01085         25        640: 1\n",
      "tensor([0.67587], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00221, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.753      0.726      0.779      0.521\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49       6.9G    0.03217    0.02904    0.01051         34        640: 1\n",
      "tensor([0.74595], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00236, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.764      0.725      0.784      0.524\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49       6.9G    0.03213    0.02901    0.01027         47        640: 1\n",
      "tensor([0.74995], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00212, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.76      0.732      0.785      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49       6.9G    0.03165    0.02852    0.01026         35        640: 1\n",
      "tensor([0.64553], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00205, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.758      0.724      0.785      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49       6.9G     0.0314    0.02879   0.009894         41        640: 1\n",
      "tensor([0.71202], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00230, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.75      0.738      0.788       0.53\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49       6.9G     0.0314     0.0285   0.009758         41        640: 1\n",
      "tensor([0.90572], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00240, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.754      0.734       0.79      0.531\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49       6.9G    0.03106     0.0284   0.009696         23        640: 1\n",
      "tensor([0.77565], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00239, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.757      0.736      0.791      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49       6.9G    0.03063    0.02805   0.009341         29        640: 1\n",
      "tensor([0.79221], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00224, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.76      0.737       0.79      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49       6.9G     0.0304    0.02804   0.009121         33        640: 1\n",
      "tensor([0.68061], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00215, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.762      0.737      0.791      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49       6.9G    0.03003    0.02764   0.008884         33        640: 1\n",
      "tensor([0.69525], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00230, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.756      0.744      0.791      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49       6.9G    0.02999    0.02746   0.008904         34        640: 1\n",
      "tensor([0.72515], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00201, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.76      0.739       0.79      0.537\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49       6.9G    0.02985    0.02735   0.008734         35        640: 1\n",
      "tensor([0.74685], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00219, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.76      0.743       0.79      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49       6.9G    0.02955    0.02739    0.00861         33        640: 1\n",
      "tensor([0.58050], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00224, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.756      0.744      0.791      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49       6.9G    0.02905    0.02691   0.008324         34        640: 1\n",
      "tensor([0.79475], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00235, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.745      0.791      0.538\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49       6.9G     0.0288    0.02674   0.008114         40        640: 1\n",
      "tensor([0.90004], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00232, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.758      0.742      0.791      0.539\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49       6.9G    0.02879    0.02674   0.007981         42        640: 1\n",
      "tensor([0.59739], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00205, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.762      0.741      0.792       0.54\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49       6.9G    0.02839     0.0266   0.007951         26        640: 1\n",
      "tensor([0.46925], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00213, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.766      0.736      0.791       0.54\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49       6.9G    0.02821     0.0267   0.007802         21        640: 1\n",
      "tensor([0.52893], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00203, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.768      0.735      0.791       0.54\n",
      "\n",
      "50 epochs completed in 2.068 hours.\n",
      "Optimizer stripped from runs/train/POD_layers_1_3_5_7_9_13_17_20_23/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/POD_layers_1_3_5_7_9_13_17_20_23/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/POD_layers_1_3_5_7_9_13_17_20_23/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.767      0.736       0.79       0.54\n",
      "                   car       4952       1201      0.818      0.886      0.917      0.696\n",
      "                person       4952       4528      0.837      0.813      0.882      0.577\n",
      "             aeroplane       4952        285      0.892        0.8       0.88      0.573\n",
      "               bicycle       4952        337      0.867      0.812      0.877      0.606\n",
      "                  bird       4952        459      0.777      0.682      0.757      0.483\n",
      "                  boat       4952        263      0.622       0.65      0.683      0.405\n",
      "                bottle       4952        469      0.673      0.704      0.729      0.496\n",
      "                   bus       4952        213       0.83      0.824      0.873      0.712\n",
      "                   cat       4952        358      0.834      0.729      0.822      0.569\n",
      "                 chair       4952        756      0.622      0.586      0.628      0.408\n",
      "                   cow       4952        244      0.755       0.82      0.828      0.592\n",
      "           diningtable       4952        206      0.723      0.626       0.71      0.463\n",
      "                   dog       4952        489      0.793      0.712       0.81      0.544\n",
      "                 horse       4952        348      0.856      0.833      0.893      0.628\n",
      "             motorbike       4952        325      0.853      0.794      0.866      0.558\n",
      "           pottedplant       4952        480      0.611      0.493      0.513      0.278\n",
      "                 sheep       4952        242      0.705      0.802      0.802      0.575\n",
      "                  sofa       4952        239      0.676      0.647      0.713      0.515\n",
      "                 train       4952        282      0.856      0.757      0.834      0.558\n",
      "             tvmonitor       4952        308      0.739      0.744      0.789      0.561\n",
      "Results saved to \u001b[1mruns/train/POD_layers_1_3_5_7_9_13_17_20_23\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : POD_layers_1_3_5_7_9_13_17_20_23\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/0e717873d11c4093abde69e0f390b647\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.843430264956798\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 28.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.8804072044102098\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.5734302840160479\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.8918466873736481\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 228.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.8384942443540383\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 42.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.8773840886852198\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.6055054634624615\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.8669203549541102\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.8118731194102113\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 274.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.7265956818970053\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 90.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.7570199955086536\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.4833366925515148\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.7768162705882284\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.6824742349796815\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 313.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.6358544745555765\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 104.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.6831334193052396\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.4054258307748791\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.6221373530132654\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.6501901140684411\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 171.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.6881946681507547\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 160.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.7288578471105778\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.49572427967957866\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6734268247215585\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.7036247334754797\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 330.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.8271358631591809\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 36.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.8732003389233277\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.7121981365925976\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.8298681891098953\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.8244214704308601\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 176.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.8508145234197778\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 236.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.9167415194403582\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.6956969095554884\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.8183780203073054\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.8859283930058285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1064.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.7782087793029484\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 52.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.8220994252327648\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.5692042696920451\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.83394296258639\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.7294575767573905\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 261.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.6036544447558827\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 269.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.628180761227262\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.407708386554139\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.6222535414827484\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.5861349297563289\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 443.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.7858387068397913\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 65.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.8284250454483713\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.5922394537663144\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.7546876332046806\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.819672131147541\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 200.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.671259985161368\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 49.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.7096816155012129\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.46326911783151753\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.723289489037088\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.6262135922330098\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 129.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.7505551492444497\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 91.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.809552672732308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.5439333550086335\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.7929049985997594\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.712499820884279\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 348.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.8443407081353936\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 49.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.8929170752883224\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.6278051925761179\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.8556427648668667\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.8333333333333334\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 290.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [5155]                 : (0.9275213479995728, 3.859363079071045)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.09894441362073886, 0.7917779093651335)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.048903686623122014, 0.5399405025101653)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.38291252561916517, 0.7676722181266877)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.1276782016389169, 0.7448765982436054)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.8224142534311517\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 44.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.866346562366149\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.5580387560503971\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.8531152674760745\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.7938461538461539\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 258.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.8248355016699054\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 719.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.8822050289542042\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.57711182368685\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.8366146985176022\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.8133833922261484\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3683.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.5457876405250345\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 151.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.5130375349985993\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.2784141483689403\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.610647575837112\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.49338296629963296\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 237.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.7500103034160591\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 81.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.802009605140512\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.5754187305696346\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.7046186720470509\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.8016528925619835\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 194.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.6610465540742243\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 74.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.7134449221763502\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.515461293369291\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.676189172677175\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.6465672895974591\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 155.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.028209591284394264, 0.0679614469408989)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.007802113424986601, 0.06525886803865433)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.026604708284139633, 0.04420400783419609)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.803032787310222\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 36.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.8342644346121912\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.5578642298558505\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.8556200349629677\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.7565353798704431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 213.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.7410964191310766\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 81.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.7893242040987049\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.5614634927619987\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.7387019187722326\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.7435064935064936\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 229.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.030631056055426598, 0.04796336218714714)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.0066903927363455296, 0.04702122136950493)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.01809621788561344, 0.02355292998254299)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07002898550724637)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : POD_layers_1_3_5_7_9_13_17_20_23\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/0e717873d11c4093abde69e0f390b647\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_old_model       : []\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Distillation_layers : [1, 3, 5, 7, 9, 13, 17, 20, 23]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_lambda          : []\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_temperature     : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Old_models          : ['./runs/train/fog_02/weights/last.pt']\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     PODNet_enable       : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     POD_lambda          : 100.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bbox_interval       : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg                 : models/yolov5s_VOCKITTI.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     data                : data/VOCKITTI.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epochs              : 50\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : POD_layers_1_3_5_7_9_13_17_20_23\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/POD_layers_1_3_5_7_9_13_17_20_23\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weights             : ./runs/train/fog_02/weights/last.pt\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.11 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for metadata to finish uploading (timeout is 3600 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still uploading 1 file(s), remaining 527.29 KB/527.29 KB\n"
     ]
    }
   ],
   "source": [
    "pod_layers = '1 3 5 7 9 13 17 20 23'\n",
    "pod_name = pod_layers.replace(' ', '_')\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_PODNet.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/last.pt \\\n",
    "--PODNet_enable \\\n",
    "--Distillation_layers {pod_layers} \\\n",
    "--POD_lambda 1e2 \\\n",
    "--Old_models \\\n",
    "   ./runs/train/fog_02/weights/last.pt \\\n",
    "--name POD_layers_{pod_name}\n",
    "\"\"\"\n",
    "!{command}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4445c88f-b8c9-4f8f-ad81-b29535f7141c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e95a3935-9e3a-4231-926e-bc9c92cb2df1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/POD_layers_1_3_5_7_9_13_17_20_23/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 7330b3e4 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.179      0.147      0.154     0.0823\n",
      "                   car       2244       8711       0.81      0.695      0.759      0.435\n",
      "                   van       2244        861          0          0          0          0\n",
      "                 truck       2244        333          0          0          0          0\n",
      "                  tram       2244        138          0          0          0          0\n",
      "                person       2244       1286      0.624      0.484       0.47      0.223\n",
      "        person_sitting       2244         89          0          0          0          0\n",
      "               cyclist       2244        496          0          0          0          0\n",
      "                  misc       2244        284          0          0          0          0\n",
      "Speed: 0.0ms pre-process, 1.0ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp311\u001b[0m\n",
      "Test set val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/POD_layers_{pod_name}/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'Test set val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5956a287-0a02-4388-809d-3c9ee3f26b2e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8be41ea7-04f0-49ff-a232-a8d31242997e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81caa98b-2e04-497b-ae15-c403da0d8593",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f038935c-eeda-483b-ab38-155106786305",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_PODNet: \u001b[0mweights=./runs/train/fog_02/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTIBiC_base.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=replay_POD_layers_1_3_5_7_9_13_17_20_23, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=False, Lwf_lambda=[], Lwf_temperature=1.0, PODNet_enable=True, Distillation_layers=[1, 3, 5, 7, 9, 13, 17, 20, 23], POD_lambda=100.0, Old_models=['./runs/train/fog_02/weights/last.pt'], DER_enable=False, DER_old_model=[]\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2895 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 7330b3e4 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/b73e27ddf8f046da88682e7c8d26388a\u001b[0m\n",
      "\n",
      "extractors长度： 0\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo_PODNet.Detect               [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/fog_02/weights/last.pt\n",
      "Overriding model.yaml nc=26 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo_PODNet.Detect               [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/kitti_old... 18599 images, \u001b[0m\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /root/autodl-tmp/datasets/VOC/labels/kitti_old.cache\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.17 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/replay_POD_layers_1_3_5_7_9_13_17_20_23/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/replay_POD_layers_1_3_5_7_9_13_17_20_23\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      4.33G    0.06666    0.04544    0.05931         64        640: 1\n",
      "tensor([1.29752], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00109, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.402      0.142      0.117     0.0578\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49       6.9G    0.04772    0.03686    0.03961         21        640: 1\n",
      "tensor([0.96386], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00189, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.412      0.394      0.333      0.172\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49       6.9G    0.04589    0.03575    0.02933         31        640: 1\n",
      "tensor([1.01947], device='cuda:0', grad_fn=<AddBackward0>) tensor(0.00255, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.487      0.489      0.466      0.234\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49       6.9G    0.04503    0.03529    0.02573         77        640:  "
     ]
    }
   ],
   "source": [
    "pod_layers = '1 3 5 7 9 13 17 20 23'\n",
    "pod_name = pod_layers.replace(' ', '_')\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_PODNet.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTIBiC_base.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/last.pt \\\n",
    "--PODNet_enable \\\n",
    "--Distillation_layers {pod_layers} \\\n",
    "--POD_lambda 1e2 \\\n",
    "--Old_models \\\n",
    "   ./runs/train/fog_02/weights/last.pt \\\n",
    "--name replay_POD_layers_{pod_name}\n",
    "\"\"\"\n",
    "!{command}\n",
    "# 11:14 创建 13:28结束\n",
    "# 2 + 14/60 = 2.233"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abf5be6f-d1ec-4db7-b450-4b7906b72399",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a50f9b03-6f12-44d8-a1d5-79ee4b14688f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/VOCKITTI.yaml, weights=['runs/train/replay_POD_layers_1_3_5_7_9_13_17_20_23/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 7330b3e4 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 image\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755       0.75      0.789      0.539\n",
      "                   car       4952       1201      0.796      0.889      0.913      0.691\n",
      "                person       4952       4528      0.825       0.82      0.875      0.571\n",
      "             aeroplane       4952        285      0.888      0.804      0.887      0.575\n",
      "               bicycle       4952        337       0.89      0.822       0.88      0.607\n",
      "                  bird       4952        459      0.749      0.675      0.725      0.467\n",
      "                  boat       4952        263      0.618      0.669      0.667       0.39\n",
      "                bottle       4952        469      0.634      0.689      0.718      0.484\n",
      "                   bus       4952        213       0.79      0.831      0.879      0.719\n",
      "                   cat       4952        358      0.824       0.76      0.811      0.559\n",
      "                 chair       4952        756      0.611      0.614      0.632      0.409\n",
      "                   cow       4952        244      0.733      0.819      0.824      0.595\n",
      "           diningtable       4952        206      0.731       0.67      0.733      0.473\n",
      "                   dog       4952        489      0.784      0.722      0.803      0.546\n",
      "                 horse       4952        348      0.829      0.868      0.897      0.626\n",
      "             motorbike       4952        325      0.825      0.785       0.86      0.556\n",
      "           pottedplant       4952        480      0.587      0.544      0.517      0.271\n",
      "                 sheep       4952        242      0.687      0.797      0.813      0.583\n",
      "                  sofa       4952        239      0.673      0.695      0.716      0.517\n",
      "                 train       4952        282      0.868      0.773      0.841       0.57\n",
      "             tvmonitor       4952        308      0.747      0.753      0.794      0.566\n",
      "Speed: 0.1ms pre-process, 1.4ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp312\u001b[0m\n",
      "VOC val successful|ly!\n",
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/replay_POD_layers_1_3_5_7_9_13_17_20_23/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 7330b3e4 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.856      0.788      0.852      0.555\n",
      "                   car       2244       8711      0.911      0.887       0.95       0.72\n",
      "                   van       2244        861      0.894      0.864      0.915      0.671\n",
      "                 truck       2244        333      0.919      0.955      0.968      0.748\n",
      "                  tram       2244        138      0.872      0.913      0.959      0.608\n",
      "                person       2244       1286      0.883      0.667      0.767      0.406\n",
      "        person_sitting       2244         89      0.609      0.506      0.568      0.279\n",
      "               cyclist       2244        496      0.856      0.736      0.829       0.47\n",
      "                  misc       2244        284      0.903      0.778      0.862      0.538\n",
      "Speed: 0.0ms pre-process, 1.1ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp313\u001b[0m\n",
      "KITTI val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/replay_POD_layers_{pod_name}/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'VOC val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'KITTI val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d498facb-b9a9-44af-a99b-4097f07af770",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65333d99-d0ff-4887-9133-4e92ed9ed01f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3d5879d-ba38-4afa-b81c-0a7e7360ce0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "96d07d16-4152-439b-9ccc-ea2fb42c63d9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_PODNet: \u001b[0mweights=./runs/train/fog_02/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTI.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=POD_Lwf_1_3_5_7_9_13_17_20_23, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=True, Lwf_lambda=[1e-05], Lwf_temperature=1.0, PODNet_enable=True, Distillation_layers=[1, 3, 5, 7, 9, 13, 17, 20, 23], POD_lambda=100.0, Old_models=['./runs/train/fog_02/weights/last.pt'], DER_enable=False, DER_old_model=[]\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2895 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 7330b3e4 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/2c9815571ee54249a3a131530b12716d\u001b[0m\n",
      "\n",
      "extractors长度： 0\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo_PODNet.Detect               [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/fog_02/weights/last.pt\n",
      "Overriding model.yaml nc=26 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo_PODNet.Detect               [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007.cache... 16551 im\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m3.99 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/POD_Lwf_1_3_5_7_9_13_17_20_23/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/POD_Lwf_1_3_5_7_9_13_17_20_23\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49       4.2G    0.06808     0.0443    0.06481         36        640: 1\n",
      "tensor([1.26447], device='cuda:0', grad_fn=<AddBackward0>) tensor(12401.63379, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.344      0.126      0.103     0.0501\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.77G    0.05017    0.03736    0.04603         58        640: 1\n",
      "tensor([1.51908], device='cuda:0', grad_fn=<AddBackward0>) tensor(11979.33398, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.368      0.369      0.318      0.165\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.77G    0.04755    0.03621    0.03343         37        640: 1\n",
      "tensor([1.11968], device='cuda:0', grad_fn=<AddBackward0>) tensor(13288.48438, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.491      0.484      0.478      0.247\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.77G    0.04574      0.036    0.02893         46        640: 1\n",
      "tensor([1.14728], device='cuda:0', grad_fn=<AddBackward0>) tensor(13602.84277, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.543       0.53       0.53      0.276\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.77G    0.04425    0.03555    0.02601         39        640: 1\n",
      "tensor([1.19761], device='cuda:0', grad_fn=<AddBackward0>) tensor(13054.37891, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.607      0.557      0.577      0.315\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.77G    0.04285    0.03506    0.02372         28        640: 1\n",
      "tensor([1.06932], device='cuda:0', grad_fn=<AddBackward0>) tensor(14411.54395, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.632       0.58      0.614      0.347\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.77G    0.04196    0.03474    0.02222         39        640: 1\n",
      "tensor([1.11138], device='cuda:0', grad_fn=<AddBackward0>) tensor(14330.85352, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.611      0.564      0.595      0.336\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.77G    0.04109    0.03422    0.02144         34        640: 1\n",
      "tensor([1.11878], device='cuda:0', grad_fn=<AddBackward0>) tensor(14960.31348, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.656      0.614      0.651      0.375\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      6.77G    0.04038    0.03385    0.02047         41        640: 1\n",
      "tensor([0.98888], device='cuda:0', grad_fn=<AddBackward0>) tensor(12796.67188, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.675      0.605      0.661      0.388\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.77G    0.03966    0.03297    0.01958         46        640: 1\n",
      "tensor([1.05241], device='cuda:0', grad_fn=<AddBackward0>) tensor(13383.08496, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.696      0.622       0.68      0.406\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.77G    0.03923    0.03323    0.01904         30        640: 1\n",
      "tensor([0.99910], device='cuda:0', grad_fn=<AddBackward0>) tensor(11358.14844, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.679      0.634      0.681      0.409\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      6.77G    0.03867    0.03321    0.01833         26        640: 1\n",
      "tensor([0.92536], device='cuda:0', grad_fn=<AddBackward0>) tensor(12373.34961, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.687       0.66      0.696      0.422\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.77G    0.03813    0.03284    0.01748         33        640: 1\n",
      "tensor([1.04289], device='cuda:0', grad_fn=<AddBackward0>) tensor(12740.51660, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.705      0.658      0.705       0.43\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.77G    0.03788     0.0326    0.01695         30        640: 1\n",
      "tensor([0.97773], device='cuda:0', grad_fn=<AddBackward0>) tensor(13059.17383, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.695       0.64      0.697       0.43\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.77G    0.03768    0.03236    0.01665         33        640: 1\n",
      "tensor([0.99412], device='cuda:0', grad_fn=<AddBackward0>) tensor(13533.92285, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.701      0.661      0.713      0.441\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.77G    0.03702    0.03219    0.01618         23        640: 1\n",
      "tensor([0.86027], device='cuda:0', grad_fn=<AddBackward0>) tensor(11634.10449, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.696      0.671      0.716      0.448\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.77G    0.03671    0.03178    0.01587         40        640: 1\n",
      "tensor([1.01199], device='cuda:0', grad_fn=<AddBackward0>) tensor(13518.27734, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.711      0.682      0.733      0.463\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.77G    0.03629    0.03178    0.01558         45        640: 1\n",
      "tensor([0.90723], device='cuda:0', grad_fn=<AddBackward0>) tensor(12968.64746, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.714      0.696      0.736      0.467\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.77G    0.03611     0.0317    0.01522         20        640: 1\n",
      "tensor([0.75019], device='cuda:0', grad_fn=<AddBackward0>) tensor(12148.85645, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.714      0.682      0.734      0.465\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.77G    0.03574    0.03158    0.01462         38        640: 1\n",
      "tensor([0.97224], device='cuda:0', grad_fn=<AddBackward0>) tensor(12496.60645, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.712      0.686      0.737      0.471\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.77G    0.03554     0.0311    0.01423         33        640: 1\n",
      "tensor([1.02398], device='cuda:0', grad_fn=<AddBackward0>) tensor(12567.26074, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.721      0.688      0.743      0.476\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.77G    0.03528    0.03107    0.01396         28        640: 1\n",
      "tensor([0.82425], device='cuda:0', grad_fn=<AddBackward0>) tensor(12427.41699, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.741      0.698      0.752      0.484\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.77G    0.03493    0.03102    0.01353         27        640: 1\n",
      "tensor([0.90966], device='cuda:0', grad_fn=<AddBackward0>) tensor(13567.51367, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.73      0.706      0.755      0.487\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      6.77G    0.03468    0.03045    0.01348         29        640: 1\n",
      "tensor([1.09518], device='cuda:0', grad_fn=<AddBackward0>) tensor(13720.47168, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.747      0.691      0.758      0.495\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.77G    0.03462     0.0306    0.01327         35        640: 1\n",
      "tensor([0.80623], device='cuda:0', grad_fn=<AddBackward0>) tensor(11832.00977, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.742      0.716      0.765      0.498\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      6.77G     0.0343    0.03034     0.0132         31        640: 1\n",
      "tensor([0.75223], device='cuda:0', grad_fn=<AddBackward0>) tensor(11845.92383, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.739      0.716      0.763      0.499\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      6.77G    0.03423    0.03013    0.01286         40        640: 1\n",
      "tensor([1.05548], device='cuda:0', grad_fn=<AddBackward0>) tensor(11289.98242, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.752      0.709      0.764        0.5\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      6.77G    0.03387    0.03041    0.01229         29        640: 1\n",
      "tensor([0.79745], device='cuda:0', grad_fn=<AddBackward0>) tensor(11192.68164, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.739      0.718      0.768      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      6.77G    0.03367    0.02992    0.01228         26        640: 1\n",
      "tensor([0.76941], device='cuda:0', grad_fn=<AddBackward0>) tensor(10782.52344, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.74      0.716      0.764      0.504\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      6.77G    0.03344     0.0301    0.01197         45        640: 1\n",
      "tensor([0.96261], device='cuda:0', grad_fn=<AddBackward0>) tensor(11873.49316, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.746      0.715      0.768      0.509\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.77G    0.03303    0.02956    0.01178         36        640: 1\n",
      "tensor([0.83137], device='cuda:0', grad_fn=<AddBackward0>) tensor(12185.99023, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.749      0.712      0.767       0.51\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.77G    0.03269    0.02909    0.01138         20        640: 1\n",
      "tensor([0.86693], device='cuda:0', grad_fn=<AddBackward0>) tensor(11549.04395, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.75      0.721      0.771      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.77G    0.03242    0.02911    0.01144         25        640: 1\n",
      "tensor([0.79381], device='cuda:0', grad_fn=<AddBackward0>) tensor(10617.93262, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.751      0.718      0.771      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.77G    0.03218    0.02914    0.01114         34        640: 1\n",
      "tensor([0.85215], device='cuda:0', grad_fn=<AddBackward0>) tensor(12092.92969, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.76      0.712      0.775      0.518\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.77G    0.03219    0.02906    0.01096         47        640: 1\n",
      "tensor([0.84891], device='cuda:0', grad_fn=<AddBackward0>) tensor(11548.40137, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.757      0.719       0.78       0.52\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      6.77G    0.03175    0.02865    0.01095         35        640: 1\n",
      "tensor([0.76820], device='cuda:0', grad_fn=<AddBackward0>) tensor(10548.10449, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.728      0.779      0.523\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      6.77G    0.03148    0.02886    0.01044         41        640: 1\n",
      "tensor([0.83506], device='cuda:0', grad_fn=<AddBackward0>) tensor(11674.95508, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.763      0.728      0.781      0.525\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      6.77G    0.03141     0.0285    0.01029         41        640: 1\n",
      "tensor([0.96298], device='cuda:0', grad_fn=<AddBackward0>) tensor(12445.49512, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.76      0.726      0.782      0.525\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      6.77G    0.03114     0.0285    0.01029         23        640: 1\n",
      "tensor([0.93007], device='cuda:0', grad_fn=<AddBackward0>) tensor(12262.00098, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.734      0.782      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      6.77G    0.03076    0.02807   0.009939         29        640: 1\n",
      "tensor([0.88019], device='cuda:0', grad_fn=<AddBackward0>) tensor(11394.18359, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.758      0.731      0.782      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      6.77G     0.0305    0.02812   0.009719         33        640: 1\n",
      "tensor([0.77684], device='cuda:0', grad_fn=<AddBackward0>) tensor(11530.03711, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.763      0.724      0.783      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      6.77G     0.0301    0.02774   0.009539         33        640: 1\n",
      "tensor([0.80504], device='cuda:0', grad_fn=<AddBackward0>) tensor(11571.85547, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.764      0.726      0.784       0.53\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      6.77G    0.03006    0.02758   0.009581         34        640: 1\n",
      "tensor([0.78002], device='cuda:0', grad_fn=<AddBackward0>) tensor(10843.37988, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.751      0.736      0.785       0.53\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      6.77G    0.02989     0.0275   0.009329         35        640: 1\n",
      "tensor([0.79584], device='cuda:0', grad_fn=<AddBackward0>) tensor(11271.35449, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.759      0.731      0.785      0.532\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      6.77G    0.02964    0.02753    0.00918         33        640: 1\n",
      "tensor([0.71167], device='cuda:0', grad_fn=<AddBackward0>) tensor(12150.25293, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.76      0.735      0.787      0.533\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      6.77G    0.02918    0.02706   0.008894         34        640: 1\n",
      "tensor([0.94174], device='cuda:0', grad_fn=<AddBackward0>) tensor(12086.26074, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.757      0.735      0.788      0.534\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      6.77G    0.02894    0.02688   0.008655         40        640: 1\n",
      "tensor([0.94144], device='cuda:0', grad_fn=<AddBackward0>) tensor(11203.76953, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.766      0.727      0.787      0.534\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      6.77G    0.02888    0.02686    0.00855         42        640: 1\n",
      "tensor([0.70024], device='cuda:0', grad_fn=<AddBackward0>) tensor(11733.53711, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.763      0.731      0.787      0.535\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      6.77G     0.0285    0.02676   0.008569         26        640: 1\n",
      "tensor([0.61083], device='cuda:0', grad_fn=<AddBackward0>) tensor(11268.17773, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.761      0.733      0.786      0.534\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      6.77G     0.0283    0.02677   0.008345         21        640: 1\n",
      "tensor([0.66935], device='cuda:0', grad_fn=<AddBackward0>) tensor(11300.80469, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.762      0.733      0.786      0.534\n",
      "\n",
      "50 epochs completed in 2.374 hours.\n",
      "Optimizer stripped from runs/train/POD_Lwf_1_3_5_7_9_13_17_20_23/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/POD_Lwf_1_3_5_7_9_13_17_20_23/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/POD_Lwf_1_3_5_7_9_13_17_20_23/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.762      0.731      0.787      0.534\n",
      "                   car       4952       1201      0.814      0.888      0.914      0.694\n",
      "                person       4952       4528      0.839      0.812      0.875       0.57\n",
      "             aeroplane       4952        285       0.88      0.796      0.882      0.569\n",
      "               bicycle       4952        337      0.886      0.787      0.888      0.607\n",
      "                  bird       4952        459      0.729      0.667      0.731      0.465\n",
      "                  boat       4952        263      0.607      0.646      0.676      0.396\n",
      "                bottle       4952        469      0.671      0.716       0.72      0.484\n",
      "                   bus       4952        213      0.832      0.779      0.862      0.702\n",
      "                   cat       4952        358      0.845      0.743      0.821      0.563\n",
      "                 chair       4952        756      0.624      0.585      0.619      0.409\n",
      "                   cow       4952        244      0.722      0.836      0.823        0.6\n",
      "           diningtable       4952        206      0.713      0.663      0.718      0.457\n",
      "                   dog       4952        489      0.823      0.674      0.809      0.531\n",
      "                 horse       4952        348      0.838      0.842       0.89      0.606\n",
      "             motorbike       4952        325      0.837      0.793      0.867      0.557\n",
      "           pottedplant       4952        480      0.624      0.518      0.536      0.281\n",
      "                 sheep       4952        242      0.695      0.782       0.82      0.593\n",
      "                  sofa       4952        239      0.676      0.607      0.688      0.496\n",
      "                 train       4952        282      0.833      0.745      0.821      0.557\n",
      "             tvmonitor       4952        308      0.759      0.737      0.773      0.553\n",
      "Results saved to \u001b[1mruns/train/POD_Lwf_1_3_5_7_9_13_17_20_23\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : POD_Lwf_1_3_5_7_9_13_17_20_23\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/2c9815571ee54249a3a131530b12716d\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.8360614828217384\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 31.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.8821124343582268\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.5686222667459749\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.8797690388670905\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.7964912280701755\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 227.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.8339129850938114\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 34.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.88809501515758\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.6073415784932124\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.8864074844074844\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.7872884754190391\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 265.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.6964280821303527\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 114.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.7310019348424034\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.46479327907004403\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.7289708904267377\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.6666666666666666\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 306.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.6259915599335031\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 110.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.6755946450942264\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.39610975660671965\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.6068430911847281\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.6463878326996197\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 170.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.6930383744539895\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 165.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.7204349481833062\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.48374907376244825\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6711365475437332\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.7164179104477612\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 336.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.8050044468025949\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 33.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.8621205625932572\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.7016720413670532\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.8324136581156977\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.7793427230046949\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 166.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.8492058567383429\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 244.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.9136984524491853\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.6938275448591757\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.8140008791557961\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.88759367194005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1066.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.7908701682260881\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 49.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.8212904922983438\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.5631744758132078\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.8453118086239105\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.7430167597765364\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 266.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.6037820855559322\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 267.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.6189224939299256\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.40927621007660314\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.6236187298763035\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.5851685018351684\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 442.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.7748166172210046\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 79.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.8232454821580201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.6003782115975282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.7219291315975777\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.8360655737704918\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 204.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.6872289746297002\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 55.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.7176155472581395\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.4573621831315694\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.7129903955002121\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.6632642351088952\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 137.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.7412480412271465\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 71.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.8089572694264006\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.5310394723600445\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.8228409550448204\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.6743768308185486\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 330.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.8401173100636691\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 57.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.88953965844246\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.6056725099815292\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.8382885932312867\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.8419540229885057\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 293.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [5155]                 : (1.037087321281433, 4.202118396759033)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.10318683208706807, 0.7877309967361388)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.05007187938535916, 0.5345960050199648)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.34418616001368346, 0.7659813244585906)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.12608848282715124, 0.7360097878373453)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.8144772839664821\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 50.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.8668627587825255\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.5569723111065731\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.8374679726471258\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.7927151767151767\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 258.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.8252754773167467\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 705.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.8748238814523241\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.5697214598130713\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.839165854888793\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.8118374558303887\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3676.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.5657632827290651\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 150.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.5358112557692704\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.2810491839927732\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.6236031815243664\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.5177421171171172\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 249.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.7362992079306202\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 83.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.8197103437683139\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.592982125682361\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.6952581050764441\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.7824895873024215\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 189.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.639680491278938\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 69.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.6875737006901884\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.4958119166231608\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.6764595376310221\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.606694560669456\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 145.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.02830338664352894, 0.06808310002088547)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.008344613015651703, 0.06481390446424484)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.026756979525089264, 0.04429638385772705)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.786956669851522\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 42.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.8205482173118539\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.5573539043561162\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.8334609312870183\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.7453677081336655\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 210.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.7479008599607941\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 72.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.7727762804071948\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.5525962697396107\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.7591152490282926\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.737012987012987\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 227.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.030820440500974655, 0.04844263568520546)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.007933720014989376, 0.046908747404813766)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.018186252564191818, 0.02363450638949871)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07002898550724637)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : POD_Lwf_1_3_5_7_9_13_17_20_23\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/2c9815571ee54249a3a131530b12716d\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_old_model       : []\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Distillation_layers : [1, 3, 5, 7, 9, 13, 17, 20, 23]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_enable          : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_lambda          : [1e-05]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_temperature     : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Old_models          : ['./runs/train/fog_02/weights/last.pt']\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     PODNet_enable       : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     POD_lambda          : 100.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bbox_interval       : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg                 : models/yolov5s_VOCKITTI.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     data                : data/VOCKITTI.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epochs              : 50\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : POD_Lwf_1_3_5_7_9_13_17_20_23\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/POD_Lwf_1_3_5_7_9_13_17_20_23\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weights             : ./runs/train/fog_02/weights/last.pt\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.11 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "pod_layers = '1 3 5 7 9 13 17 20 23'\n",
    "pod_name = pod_layers.replace(' ', '_')\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_PODNet.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/last.pt \\\n",
    "--Lwf_enable \\\n",
    "--Lwf_temperature 1.0 \\\n",
    "--Lwf_lambda 1e-5 \\\n",
    "--PODNet_enable \\\n",
    "--Distillation_layers {pod_layers} \\\n",
    "--POD_lambda 1e2 \\\n",
    "--Old_models \\\n",
    "   ./runs/train/fog_02/weights/last.pt \\\n",
    "--name POD_Lwf_{pod_name}\n",
    "\"\"\"\n",
    "!{command}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1533146e-a07d-4941-bfaa-ce134c4a230d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/VOCKITTI.yaml, weights=['runs/train/POD_Lwf_1_3_5_7_9_13_17_20_23/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 7330b3e4 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 image\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.762      0.733      0.786      0.534\n",
      "                   car       4952       1201      0.813      0.887      0.913      0.694\n",
      "                person       4952       4528      0.838      0.811      0.875      0.569\n",
      "             aeroplane       4952        285      0.894      0.803      0.883      0.571\n",
      "               bicycle       4952        337      0.892      0.783      0.891       0.61\n",
      "                  bird       4952        459       0.73      0.664      0.731      0.465\n",
      "                  boat       4952        263      0.609      0.658      0.673      0.397\n",
      "                bottle       4952        469      0.658      0.712      0.713       0.48\n",
      "                   bus       4952        213      0.831      0.785      0.861      0.703\n",
      "                   cat       4952        358       0.84      0.745      0.817       0.56\n",
      "                 chair       4952        756      0.623      0.585       0.62       0.41\n",
      "                   cow       4952        244      0.731      0.832      0.825      0.598\n",
      "           diningtable       4952        206      0.709      0.664      0.717      0.456\n",
      "                   dog       4952        489      0.818      0.681      0.804      0.528\n",
      "                 horse       4952        348       0.84      0.839      0.884      0.603\n",
      "             motorbike       4952        325      0.842      0.803      0.867      0.562\n",
      "           pottedplant       4952        480      0.617      0.515      0.537       0.28\n",
      "                 sheep       4952        242      0.698      0.793       0.82      0.592\n",
      "                  sofa       4952        239      0.667      0.611      0.691      0.496\n",
      "                 train       4952        282      0.842      0.766      0.832      0.563\n",
      "             tvmonitor       4952        308       0.75      0.731      0.774      0.553\n",
      "Speed: 0.1ms pre-process, 1.5ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp314\u001b[0m\n",
      "VOC val successful|ly!\n",
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/POD_Lwf_1_3_5_7_9_13_17_20_23/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 7330b3e4 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.217      0.406      0.306      0.163\n",
      "                   car       2244       8711      0.165      0.918       0.77       0.44\n",
      "                   van       2244        861      0.263      0.278      0.219      0.136\n",
      "                 truck       2244        333      0.341        0.3      0.283        0.2\n",
      "                  tram       2244        138      0.181      0.536      0.199      0.102\n",
      "                person       2244       1286     0.0509      0.722      0.476       0.23\n",
      "        person_sitting       2244         89      0.385     0.0562      0.209     0.0911\n",
      "               cyclist       2244        496      0.196      0.317      0.177     0.0521\n",
      "                  misc       2244        284      0.153       0.12      0.112     0.0502\n",
      "Speed: 0.0ms pre-process, 1.0ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp315\u001b[0m\n",
      "KITTI val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/POD_Lwf_{pod_name}/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'VOC val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'KITTI val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83745c15-a021-41ff-95d7-1e98970a9ecb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "522a1f85-1a71-43b1-97be-3cf14392dab5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "65285d5b-b7b8-4e78-980b-8fa29ce79308",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9442672-e8ef-435b-9993-5e1269ef4ec5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_PODNet: \u001b[0mweights=./runs/train/fog_02/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTI.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=POD_Lwf_1_3_5_7_9_13_17_20_23_1e-4, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=True, Lwf_lambda=[0.0001], Lwf_temperature=1.0, PODNet_enable=True, Distillation_layers=[1, 3, 5, 7, 9, 13, 17, 20, 23], POD_lambda=100.0, Old_models=['./runs/train/fog_02/weights/last.pt'], DER_enable=False, DER_old_model=[]\n",
      "Command 'git fetch ultralytics' timed out after 5 seconds\n",
      "YOLOv5 🚀 7330b3e4 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/ccf642b115ac471eaaefd9ac10786678\u001b[0m\n",
      "\n",
      "extractors长度： 0\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo_PODNet.Detect               [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/fog_02/weights/last.pt\n",
      "Overriding model.yaml nc=26 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo_PODNet.Detect               [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/train2007.cache... 16551 im\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m3.99 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/POD_Lwf_1_3_5_7_9_13_17_20_23_1e-4/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/POD_Lwf_1_3_5_7_9_13_17_20_23_1e-4\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49      4.34G    0.08098    0.04734    0.07158         85        640:  fatal: unable to access 'https://github.com/ultralytics/yolov5/': GnuTLS recv error (-110): The TLS connection was non-properly terminated.\n",
      "       0/49      4.34G    0.07042    0.04537    0.06592         36        640: 1\n",
      "tensor([1.56001], device='cuda:0', grad_fn=<AddBackward0>) tensor(3592.13452, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.444     0.0966      0.085     0.0382\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.92G    0.05301    0.03996    0.05283         58        640: 1\n",
      "tensor([1.85562], device='cuda:0', grad_fn=<AddBackward0>) tensor(4759.17529, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.298      0.245      0.198     0.0927\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.92G    0.04984    0.03869    0.04234         37        640: 1\n",
      "tensor([1.65631], device='cuda:0', grad_fn=<AddBackward0>) tensor(6939.46045, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.347      0.368      0.311       0.15\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.92G    0.04727    0.03741     0.0354         46        640: 1\n",
      "tensor([1.78726], device='cuda:0', grad_fn=<AddBackward0>) tensor(7725.16309, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.485      0.439      0.432      0.217\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.92G    0.04568     0.0366    0.03091         39        640: 1\n",
      "tensor([1.84106], device='cuda:0', grad_fn=<AddBackward0>) tensor(7555.10889, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.539      0.496      0.502      0.261\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.92G    0.04408    0.03602    0.02818         28        640: 1\n",
      "tensor([1.71991], device='cuda:0', grad_fn=<AddBackward0>) tensor(8536.92969, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.566      0.523      0.539      0.289\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.92G    0.04322    0.03582    0.02653         39        640: 1\n",
      "tensor([1.77642], device='cuda:0', grad_fn=<AddBackward0>) tensor(8713.35547, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.569      0.539      0.553        0.3\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.92G    0.04244    0.03525    0.02561         34        640: 1\n",
      "tensor([1.92862], device='cuda:0', grad_fn=<AddBackward0>) tensor(9522.56250, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.599      0.557       0.58      0.318\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      6.92G    0.04172    0.03495    0.02469         41        640: 1\n",
      "tensor([1.57228], device='cuda:0', grad_fn=<AddBackward0>) tensor(7701.87646, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.608      0.565      0.596      0.334\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.92G    0.04115    0.03412     0.0236         46        640: 1\n",
      "tensor([1.66620], device='cuda:0', grad_fn=<AddBackward0>) tensor(7986.71143, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.642      0.563      0.609      0.344\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.92G    0.04072    0.03437    0.02283         30        640: 1\n",
      "tensor([1.68253], device='cuda:0', grad_fn=<AddBackward0>) tensor(7386.23047, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.645      0.584      0.619      0.358\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      6.92G    0.04022    0.03445    0.02228         26        640: 1\n",
      "tensor([1.59182], device='cuda:0', grad_fn=<AddBackward0>) tensor(8180.17920, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.641      0.594      0.633      0.363\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.92G    0.03967    0.03415     0.0215         33        640: 1\n",
      "tensor([1.62636], device='cuda:0', grad_fn=<AddBackward0>) tensor(8056.36133, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.623      0.591      0.617      0.356\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.92G    0.03949    0.03395    0.02107         30        640: 1\n",
      "tensor([1.63825], device='cuda:0', grad_fn=<AddBackward0>) tensor(8141.73340, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.665      0.591      0.646      0.378\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.92G    0.03917    0.03372    0.02058         33        640: 1\n",
      "tensor([1.75958], device='cuda:0', grad_fn=<AddBackward0>) tensor(9116.12598, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.638      0.622      0.647      0.382\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.92G    0.03872    0.03355    0.02016         23        640: 1\n",
      "tensor([1.34376], device='cuda:0', grad_fn=<AddBackward0>) tensor(6933.00928, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.642      0.622       0.65      0.385\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.92G    0.03836    0.03323    0.01991         40        640: 1\n",
      "tensor([1.78028], device='cuda:0', grad_fn=<AddBackward0>) tensor(8406.94531, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.658      0.624      0.667      0.398\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.92G    0.03782    0.03326     0.0193         45        640: 1\n",
      "tensor([1.54101], device='cuda:0', grad_fn=<AddBackward0>) tensor(7526.33008, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.666      0.626      0.667      0.397\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.92G    0.03776    0.03318    0.01905         20        640: 1\n",
      "tensor([1.36734], device='cuda:0', grad_fn=<AddBackward0>) tensor(7710.48193, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.666      0.633      0.674      0.405\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.92G    0.03746    0.03306    0.01857         38        640: 1\n",
      "tensor([1.60282], device='cuda:0', grad_fn=<AddBackward0>) tensor(7724.35889, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.671      0.627      0.671      0.407\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.92G    0.03721     0.0326    0.01816         33        640: 1\n",
      "tensor([1.53031], device='cuda:0', grad_fn=<AddBackward0>) tensor(7090.01221, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.668      0.636      0.677      0.412\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.92G    0.03702    0.03272    0.01796         28        640: 1\n",
      "tensor([1.45201], device='cuda:0', grad_fn=<AddBackward0>) tensor(7382.13721, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.67      0.643      0.684      0.417\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.92G    0.03671    0.03271     0.0174         27        640: 1\n",
      "tensor([1.52490], device='cuda:0', grad_fn=<AddBackward0>) tensor(7954.62598, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.684      0.638       0.69      0.421\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.92G    0.03634    0.03227    0.01703         35        640: 1\n",
      "tensor([1.34193], device='cuda:0', grad_fn=<AddBackward0>) tensor(7256.66602, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.688      0.658      0.701      0.432\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.92G    0.03491    0.03151    0.01557         36        640: 1\n",
      "tensor([1.41013], device='cuda:0', grad_fn=<AddBackward0>) tensor(7304.81201, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.706      0.657      0.708      0.442\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.92G    0.03461    0.03104    0.01537         20        640: 1\n",
      "tensor([1.50409], device='cuda:0', grad_fn=<AddBackward0>) tensor(7044.51758, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.689      0.664      0.705      0.441\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.92G    0.03437    0.03115    0.01547         25        640: 1\n",
      "tensor([1.26155], device='cuda:0', grad_fn=<AddBackward0>) tensor(6207.17383, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.698      0.659      0.705      0.441\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.92G     0.0342    0.03121    0.01514         34        640: 1\n",
      "tensor([1.37176], device='cuda:0', grad_fn=<AddBackward0>) tensor(7010.10986, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.699       0.67      0.709      0.444\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.92G    0.03416     0.0312     0.0149         47        640: 1\n",
      "tensor([1.36237], device='cuda:0', grad_fn=<AddBackward0>) tensor(6290.97852, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.713      0.661      0.711      0.445\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      6.92G    0.03374    0.03079    0.01482         35        640: 1\n",
      "tensor([1.24662], device='cuda:0', grad_fn=<AddBackward0>) tensor(6095.05957, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.705      0.665      0.714      0.448\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      6.92G    0.03353     0.0311    0.01457         41        640: 1\n",
      "tensor([1.41037], device='cuda:0', grad_fn=<AddBackward0>) tensor(6890.47314, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.708      0.666      0.714      0.449\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      6.92G    0.03364    0.03085    0.01438         41        640: 1\n",
      "tensor([1.56473], device='cuda:0', grad_fn=<AddBackward0>) tensor(7121.73486, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.709      0.666      0.714      0.447\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      6.92G    0.03331    0.03079    0.01441         23        640: 1\n",
      "tensor([1.46848], device='cuda:0', grad_fn=<AddBackward0>) tensor(6866.40576, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032        0.7       0.67      0.712      0.448\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      6.92G     0.0329    0.03043    0.01401         29        640: 1\n",
      "tensor([1.39737], device='cuda:0', grad_fn=<AddBackward0>) tensor(6690.32910, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.71      0.666      0.713      0.449\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      6.92G    0.03278    0.03054    0.01373         33        640: 1\n",
      "tensor([1.31020], device='cuda:0', grad_fn=<AddBackward0>) tensor(6330.87158, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.707      0.666      0.713      0.451\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      6.92G    0.03246    0.03026    0.01363         33        640: 1\n",
      "tensor([1.34815], device='cuda:0', grad_fn=<AddBackward0>) tensor(6483.33887, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032        0.7      0.673      0.714      0.451\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      6.92G    0.03249    0.03007    0.01361         34        640: 1\n",
      "tensor([1.28163], device='cuda:0', grad_fn=<AddBackward0>) tensor(5889.04688, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.706       0.67      0.714      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      6.92G    0.03226    0.02999    0.01341         35        640: 1\n",
      "tensor([1.34942], device='cuda:0', grad_fn=<AddBackward0>) tensor(6251.38135, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.706      0.675      0.714      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      6.92G    0.03213    0.03012    0.01325         33        640: 1\n",
      "tensor([1.26541], device='cuda:0', grad_fn=<AddBackward0>) tensor(6630.70117, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.708      0.675      0.715      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      6.92G    0.03168    0.02969    0.01294         34        640: 1\n",
      "tensor([1.49025], device='cuda:0', grad_fn=<AddBackward0>) tensor(6639.26807, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.708      0.673      0.714      0.451\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      6.92G    0.03145    0.02958    0.01268         40        640: 1\n",
      "tensor([1.45181], device='cuda:0', grad_fn=<AddBackward0>) tensor(5841.74756, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.71      0.666      0.714      0.451\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      6.92G    0.03145    0.02961    0.01266         42        640: 1\n",
      "tensor([1.22436], device='cuda:0', grad_fn=<AddBackward0>) tensor(6436.29688, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.71       0.67      0.715      0.453\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      6.92G     0.0311    0.02954    0.01276         26        640: 1\n",
      "tensor([1.12278], device='cuda:0', grad_fn=<AddBackward0>) tensor(6365.32422, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.711      0.668      0.714      0.452\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      6.92G    0.03085    0.02959    0.01245         21        640: 1\n",
      "tensor([1.14262], device='cuda:0', grad_fn=<AddBackward0>) tensor(6080.37842, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.703      0.674      0.713      0.452\n",
      "\n",
      "50 epochs completed in 2.367 hours.\n",
      "Optimizer stripped from runs/train/POD_Lwf_1_3_5_7_9_13_17_20_23_1e-4/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/POD_Lwf_1_3_5_7_9_13_17_20_23_1e-4/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/POD_Lwf_1_3_5_7_9_13_17_20_23_1e-4/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.71      0.671      0.715      0.453\n",
      "                   car       4952       1201      0.763      0.851      0.878      0.644\n",
      "                person       4952       4528      0.798      0.782      0.839      0.516\n",
      "             aeroplane       4952        285      0.876      0.722      0.832      0.497\n",
      "               bicycle       4952        337       0.83      0.726      0.827      0.531\n",
      "                  bird       4952        459      0.675      0.588      0.617      0.354\n",
      "                  boat       4952        263      0.532      0.582      0.554      0.301\n",
      "                bottle       4952        469      0.631      0.657      0.664      0.422\n",
      "                   bus       4952        213      0.766      0.756      0.797      0.611\n",
      "                   cat       4952        358      0.778      0.637      0.713      0.426\n",
      "                 chair       4952        756      0.569      0.521      0.548      0.333\n",
      "                   cow       4952        244      0.661       0.76      0.755       0.53\n",
      "           diningtable       4952        206      0.652      0.583      0.651      0.384\n",
      "                   dog       4952        489      0.688      0.568      0.656      0.386\n",
      "                 horse       4952        348      0.815      0.805       0.84      0.532\n",
      "             motorbike       4952        325      0.774      0.728      0.808      0.483\n",
      "           pottedplant       4952        480      0.576      0.461      0.455       0.22\n",
      "                 sheep       4952        242      0.637      0.752      0.763      0.528\n",
      "                  sofa       4952        239      0.666      0.536      0.625      0.412\n",
      "                 train       4952        282      0.786       0.73      0.768      0.481\n",
      "             tvmonitor       4952        308      0.731      0.669        0.7      0.461\n",
      "Results saved to \u001b[1mruns/train/POD_Lwf_1_3_5_7_9_13_17_20_23_1e-4\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : POD_Lwf_1_3_5_7_9_13_17_20_23_1e-4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/exp-100epoch/ccf642b115ac471eaaefd9ac10786678\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.7918119181697431\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 29.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.83208365029819\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.49710179927320575\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.8764832499347867\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.7220585497778481\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 206.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.7745234218627676\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 50.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.8267329460619259\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.5311479375357752\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.8302727570954636\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.7257896868183713\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 245.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.6287964897786461\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 130.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.6174029990926158\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.35425544940623777\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.6753656766814662\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.5882352941176471\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 270.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.5557850920572852\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 135.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.5544513049610358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.301407441473149\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.5320397052104369\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.5817490494296578\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 153.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.6437013028556245\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 180.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.6640371916118002\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.42163241498591714\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6311920418977117\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.6567164179104478\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 308.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.7610567617404179\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 49.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.7974214030636837\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.6114306315584097\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.7663166940856954\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.755868544600939\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 161.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.8044436105974558\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 318.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.8784377382048623\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.643766703382318\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.7627511097645862\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.8509575353871773\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1022.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.7005087383972466\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 65.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.7132866230769602\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.4263054403279985\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.7782752596909406\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.6368715083798883\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 228.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.5442060531482508\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 298.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.548388885861637\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.3330617428051013\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.5693798329753592\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.5211640211640212\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 394.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.7070110720028447\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 95.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.7549724427939632\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.5301125896044796\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.6611570125418184\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.7596994713887087\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 185.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.6153963495751869\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 64.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.6507758009887236\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.38399181077249217\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.6522002718644835\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.5825242718446602\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 120.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.6220373614667606\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 126.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.6556790925349189\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.3855965220436704\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.687826501783704\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.5677347422405364\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 278.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.8095264396683016\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 64.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.8401916533470537\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.5322238925534356\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.8145159344864032\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.8045977011494253\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 280.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [5155]                 : (1.4685146808624268, 7.286914825439453)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.08504780437968687, 0.7148227835728898)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.03818564957623554, 0.4527715660637024)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.2983153001170411, 0.7127961799074828)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.09658599539103213, 0.6752816445429557)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.7503301257944949\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 69.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.8081598599119887\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.48343314801063286\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.7741894406230633\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.727897457897458\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 237.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.7899258268280641\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 895.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.8385339926066965\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.5162623566289436\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.7982201331054789\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.7818021201413428\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3540.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.5118227863708477\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 163.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.4549415057515751\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.22016946917040547\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.5756801263745709\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.460717662106551\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 221.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.690039813018815\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 104.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.7631287688271386\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.5282359745095693\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.6374651699691571\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.7520661157024794\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 182.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.5937709163770939\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 64.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.6253710918982132\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.4120656807834422\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.6661715255843061\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.5355648535564853\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 128.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.030849311500787735, 0.07042157649993896)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.012449629604816437, 0.06592187285423279)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.02954445779323578, 0.04537498578429222)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.7568334663045149\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 56.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.7678188508753522\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.48095749157347034\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.7860734757578957\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.729690756033208\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 206.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.6983182622127458\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 76.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.7002398276228303\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.46074521246114336\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.7305252953246862\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.6688311688311688\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 206.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.03320220485329628, 0.05220270901918411)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.01173041108995676, 0.050354331731796265)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.019429778680205345, 0.02521151304244995)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07002898550724637)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.00960090692431562)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : POD_Lwf_1_3_5_7_9_13_17_20_23_1e-4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/exp-100epoch/ccf642b115ac471eaaefd9ac10786678\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_old_model       : []\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Distillation_layers : [1, 3, 5, 7, 9, 13, 17, 20, 23]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_enable          : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_lambda          : [0.0001]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_temperature     : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Old_models          : ['./runs/train/fog_02/weights/last.pt']\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     PODNet_enable       : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     POD_lambda          : 100.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bbox_interval       : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg                 : models/yolov5s_VOCKITTI.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     data                : data/VOCKITTI.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epochs              : 50\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : POD_Lwf_1_3_5_7_9_13_17_20_23_1e-4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/POD_Lwf_1_3_5_7_9_13_17_20_23_1e-4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weights             : ./runs/train/fog_02/weights/last.pt\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.28 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for assets to finish uploading (timeout is 10800 seconds)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still uploading 8 file(s), remaining 1.68 MB/3.55 MB\n"
     ]
    }
   ],
   "source": [
    "pod_layers = '1 3 5 7 9 13 17 20 23'\n",
    "pod_name = pod_layers.replace(' ', '_')\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_PODNet.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/last.pt \\\n",
    "--Lwf_enable \\\n",
    "--Lwf_temperature 1.0 \\\n",
    "--Lwf_lambda 1e-4 \\\n",
    "--PODNet_enable \\\n",
    "--Distillation_layers {pod_layers} \\\n",
    "--POD_lambda 1e2 \\\n",
    "--Old_models \\\n",
    "   ./runs/train/fog_02/weights/last.pt \\\n",
    "--name POD_Lwf_{pod_name}_1e-4\n",
    "\"\"\"\n",
    "!{command}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c957900c-44cb-4281-aeac-9e516ecd7dc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/VOCKITTI.yaml, weights=['runs/train/POD_Lwf_1_3_5_7_9_13_17_20_23_1e-4/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 7330b3e4 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 image\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.703      0.673      0.713      0.452\n",
      "                   car       4952       1201       0.76      0.854      0.877      0.642\n",
      "                person       4952       4528      0.795      0.786      0.838      0.516\n",
      "             aeroplane       4952        285      0.871      0.734       0.84        0.5\n",
      "               bicycle       4952        337      0.812      0.739      0.827      0.534\n",
      "                  bird       4952        459      0.669      0.595      0.619      0.358\n",
      "                  boat       4952        263      0.521      0.586      0.551      0.299\n",
      "                bottle       4952        469      0.621      0.663      0.666      0.422\n",
      "                   bus       4952        213      0.761      0.746      0.794      0.609\n",
      "                   cat       4952        358      0.772       0.64      0.712      0.422\n",
      "                 chair       4952        756      0.568       0.53      0.548      0.333\n",
      "                   cow       4952        244       0.66      0.765      0.752      0.528\n",
      "           diningtable       4952        206      0.656      0.603      0.649      0.383\n",
      "                   dog       4952        489      0.676      0.564      0.649      0.381\n",
      "                 horse       4952        348       0.82      0.807      0.843      0.534\n",
      "             motorbike       4952        325      0.764      0.735      0.808      0.482\n",
      "           pottedplant       4952        480      0.557       0.46      0.445      0.218\n",
      "                 sheep       4952        242      0.643      0.748      0.762      0.528\n",
      "                  sofa       4952        239      0.649      0.519       0.62      0.408\n",
      "                 train       4952        282      0.769      0.721      0.764      0.482\n",
      "             tvmonitor       4952        308      0.725      0.668      0.702      0.462\n",
      "Speed: 0.1ms pre-process, 1.5ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp318\u001b[0m\n",
      "VOC val successful|ly!\n",
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/POD_Lwf_1_3_5_7_9_13_17_20_23_1e-4/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 7330b3e4 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.685      0.286      0.529      0.262\n",
      "                   car       2244       8711      0.657      0.827      0.814      0.484\n",
      "                   van       2244        861      0.884      0.281      0.588      0.328\n",
      "                 truck       2244        333      0.929      0.196      0.756      0.423\n",
      "                  tram       2244        138      0.643     0.0135      0.657      0.299\n",
      "                person       2244       1286      0.388      0.617      0.524      0.255\n",
      "        person_sitting       2244         89      0.327      0.236      0.196     0.0569\n",
      "               cyclist       2244        496        0.8     0.0967      0.377      0.104\n",
      "                  misc       2244        284      0.852     0.0204      0.323      0.148\n",
      "Speed: 0.0ms pre-process, 1.0ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp319\u001b[0m\n",
      "KITTI val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/POD_Lwf_{pod_name}_1e-4/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'VOC val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'KITTI val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf56c1be-f2d0-47a9-9110-575b5ea491f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "562eb535-8b83-4017-8c2d-78b6fab3d98d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95161983-10ab-4ccc-8b68-0e5b889ffab5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de512312-c144-41e7-9391-c65a5b30db87",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0ccaf6ae-0c5b-499a-9097-fb6c9533f027",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mtrain_PODNet: \u001b[0mweights=./runs/train/fog_02/weights/last.pt, cfg=models/yolov5s_VOCKITTI.yaml, data=data/VOCKITTIBiC_base.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, evolve_population=data/hyps, resume_evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs/train, name=replay_POD_Lwf_1_3_5_7_9_13_17_20_23, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=1, artifact_alias=latest, ndjson_console=False, ndjson_file=False, ewc_pt=None, ewc_lambda=0.0, SI_enable=False, SI_pt=None, SI_lambda=10.0, Lwf_enable=True, Lwf_lambda=[1e-05], Lwf_temperature=1.0, PODNet_enable=True, Distillation_layers=[1, 3, 5, 7, 9, 13, 17, 20, 23], POD_lambda=100.0, Old_models=['./runs/train/fog_02/weights/last.pt'], DER_enable=False, DER_old_model=[]\n",
      "\u001b[34m\u001b[1mgithub: \u001b[0m⚠️ YOLOv5 is out of date by 2895 commits. Use 'git pull ultralytics master' or 'git clone https://github.com/ultralytics/yolov5' to update.\n",
      "YOLOv5 🚀 b2fc1a21 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
      "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/3cd2373a500446f5b236cb06b17813ae\u001b[0m\n",
      "\n",
      "extractors长度： 0\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     83613  models.yolo_PODNet.Detect               [26, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7089757 parameters, 7089757 gradients, 16.2 GFLOPs\n",
      "\n",
      "Transferred 342/355 items from runs/train/fog_02/weights/last.pt\n",
      "Overriding model.yaml nc=26 with nc=8\n",
      "\n",
      "                 from  n    params  module                                  arguments                     \n",
      "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
      "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
      "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
      "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
      "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
      "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
      "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
      "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
      "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
      "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
      " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
      " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
      " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
      " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
      " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
      " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
      " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
      " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
      " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
      " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
      " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
      " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
      " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
      " 24      [17, 20, 23]  1     35067  models.yolo_PODNet.Detect               [8, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
      "YOLOv5s_VOCKITTI summary: 217 layers, 7041211 parameters, 7041211 gradients, 16.0 GFLOPs\n",
      "\n",
      "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
      "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 66 weight(decay=0.0005), 60 bias\n",
      "\u001b[34m\u001b[1malbumentations: \u001b[0m1 validation error for InitSchema\n",
      "size\n",
      "  Field required [type=missing, input_value={'height': 640, 'width': ...'mask_interpolation': 0}, input_type=dict]\n",
      "    For further information visit https://errors.pydantic.dev/2.10/v/missing\n",
      "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/kitti_old.cache... 18599 im\u001b[0m\n",
      "\u001b[34m\u001b[1mval: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 images\u001b[0m\n",
      "\n",
      "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.17 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
      "Plotting labels to runs/train/replay_POD_Lwf_1_3_5_7_9_13_17_20_23/labels.jpg... \n",
      "Image sizes 640 train, 640 val\n",
      "Using 8 dataloader workers\n",
      "Logging results to \u001b[1mruns/train/replay_POD_Lwf_1_3_5_7_9_13_17_20_23\u001b[0m\n",
      "Starting training for 50 epochs...\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       0/49       4.2G    0.06686     0.0456    0.05841         64        640: 1\n",
      "tensor([1.38704], device='cuda:0', grad_fn=<AddBackward0>) tensor(9811.63965, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.362      0.138      0.115     0.0564\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       1/49      6.77G    0.04783    0.03711    0.03894         21        640: 1\n",
      "tensor([1.05718], device='cuda:0', grad_fn=<AddBackward0>) tensor(10306.24414, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.383      0.379      0.308      0.157\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       2/49      6.77G     0.0458    0.03594     0.0296         31        640: 1\n",
      "tensor([1.12131], device='cuda:0', grad_fn=<AddBackward0>) tensor(12096.61035, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.487      0.479      0.458      0.224\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       3/49      6.77G    0.04444    0.03549    0.02503         29        640: 1\n",
      "tensor([0.88720], device='cuda:0', grad_fn=<AddBackward0>) tensor(11356.63574, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.543      0.513      0.514      0.274\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       4/49      6.77G     0.0432    0.03531     0.0226         44        640: 1\n",
      "tensor([0.98393], device='cuda:0', grad_fn=<AddBackward0>) tensor(10521.55469, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.628      0.563      0.595      0.325\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       5/49      6.77G    0.04198    0.03491    0.02085         30        640: 1\n",
      "tensor([0.95287], device='cuda:0', grad_fn=<AddBackward0>) tensor(12670.15430, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.651       0.58      0.627      0.355\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       6/49      6.77G    0.04103     0.0341    0.01922         44        640: 1\n",
      "tensor([0.93382], device='cuda:0', grad_fn=<AddBackward0>) tensor(10695.47363, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.629      0.603       0.63      0.358\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       7/49      6.77G    0.04021    0.03385    0.01828         41        640: 1\n",
      "tensor([0.92103], device='cuda:0', grad_fn=<AddBackward0>) tensor(11316.65723, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.653      0.608      0.654      0.383\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       8/49      6.77G    0.03974    0.03343    0.01763         35        640: 1\n",
      "tensor([0.82200], device='cuda:0', grad_fn=<AddBackward0>) tensor(10863.64844, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.678      0.633      0.675      0.403\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "       9/49      6.77G    0.03913    0.03328    0.01677         33        640: 1\n",
      "tensor([0.84376], device='cuda:0', grad_fn=<AddBackward0>) tensor(10448.54102, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.692       0.65      0.698      0.422\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      10/49      6.77G    0.03863     0.0333    0.01612         59        640: 1\n",
      "tensor([0.88584], device='cuda:0', grad_fn=<AddBackward0>) tensor(9599.43945, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.696      0.641      0.694       0.42\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      11/49      6.77G    0.03817    0.03301    0.01576         34        640: 1\n",
      "tensor([0.88384], device='cuda:0', grad_fn=<AddBackward0>) tensor(10666.27539, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032        0.7       0.65      0.699       0.43\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      12/49      6.77G    0.03791    0.03261     0.0153         56        640: 1\n",
      "tensor([1.01983], device='cuda:0', grad_fn=<AddBackward0>) tensor(10721.63477, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.707      0.664      0.719      0.445\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      13/49      6.77G    0.03748     0.0324    0.01486         36        640: 1\n",
      "tensor([0.89557], device='cuda:0', grad_fn=<AddBackward0>) tensor(10556.96582, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.69      0.667       0.71      0.441\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      14/49      6.77G    0.03697    0.03211    0.01441         69        640: 1\n",
      "tensor([1.06518], device='cuda:0', grad_fn=<AddBackward0>) tensor(9840.57324, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.708      0.665      0.719       0.45\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      15/49      6.77G    0.03669      0.032    0.01392         51        640: 1\n",
      "tensor([0.90919], device='cuda:0', grad_fn=<AddBackward0>) tensor(9955.15137, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.716      0.671      0.727       0.46\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      16/49      6.77G    0.03649    0.03192    0.01356         43        640: 1\n",
      "tensor([0.92876], device='cuda:0', grad_fn=<AddBackward0>) tensor(10710.62793, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.719      0.686      0.737       0.47\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      17/49      6.77G    0.03635    0.03169    0.01335         27        640: 1\n",
      "tensor([0.86672], device='cuda:0', grad_fn=<AddBackward0>) tensor(12185.42969, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.711      0.694      0.737       0.47\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      18/49      6.77G    0.03602    0.03153    0.01326         33        640: 1\n",
      "tensor([0.93858], device='cuda:0', grad_fn=<AddBackward0>) tensor(10337.26465, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.728      0.689       0.74      0.473\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      19/49      6.77G    0.03585    0.03161    0.01292         46        640: 1\n",
      "tensor([0.90752], device='cuda:0', grad_fn=<AddBackward0>) tensor(9612.25586, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.719        0.7      0.745      0.478\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      20/49      6.77G    0.03551    0.03103    0.01266         46        640: 1\n",
      "tensor([0.96534], device='cuda:0', grad_fn=<AddBackward0>) tensor(12142.39355, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.729      0.699       0.75      0.484\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      21/49      6.77G    0.03536    0.03075    0.01241         29        640: 1\n",
      "tensor([0.97908], device='cuda:0', grad_fn=<AddBackward0>) tensor(11337.73145, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.723      0.701      0.745      0.483\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      22/49      6.77G    0.03508    0.03075    0.01225         35        640: 1\n",
      "tensor([0.87565], device='cuda:0', grad_fn=<AddBackward0>) tensor(10785.43945, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.724      0.699       0.75      0.487\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      23/49      6.77G    0.03494    0.03086    0.01207         37        640: 1\n",
      "tensor([0.76715], device='cuda:0', grad_fn=<AddBackward0>) tensor(9730.75977, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.75      0.694      0.758      0.495\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      24/49      6.77G    0.03441    0.03073    0.01153         31        640: 1\n",
      "tensor([0.77861], device='cuda:0', grad_fn=<AddBackward0>) tensor(11162.16406, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.73      0.705      0.753      0.493\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      25/49      6.77G    0.03428    0.03012    0.01142         47        640: 1\n",
      "tensor([1.03795], device='cuda:0', grad_fn=<AddBackward0>) tensor(11907.10449, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.732      0.715      0.764      0.501\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      26/49      6.77G     0.0342    0.03038    0.01134         40        640: 1\n",
      "tensor([0.72590], device='cuda:0', grad_fn=<AddBackward0>) tensor(10038.99512, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.738      0.713      0.768      0.504\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      27/49      6.77G    0.03387       0.03    0.01118         31        640: 1\n",
      "tensor([0.81002], device='cuda:0', grad_fn=<AddBackward0>) tensor(11515.25781, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.722       0.72      0.765      0.507\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      28/49      6.77G    0.03368    0.03004    0.01101         26        640: 1\n",
      "tensor([0.69074], device='cuda:0', grad_fn=<AddBackward0>) tensor(10179.42578, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.74       0.72       0.77      0.509\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      29/49      6.77G    0.03339    0.02968    0.01067         33        640: 1\n",
      "tensor([0.94669], device='cuda:0', grad_fn=<AddBackward0>) tensor(10954.75488, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.741      0.714      0.769       0.51\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      30/49      6.77G     0.0333    0.02995    0.01077         40        640: 1\n",
      "tensor([0.89221], device='cuda:0', grad_fn=<AddBackward0>) tensor(10621.62988, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.743      0.716      0.771      0.511\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      31/49      6.77G    0.03291    0.02959    0.01023         40        640: 1\n",
      "tensor([0.79426], device='cuda:0', grad_fn=<AddBackward0>) tensor(11659.18555, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.742      0.721       0.77      0.513\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      32/49      6.77G    0.03283    0.02975    0.01017         33        640: 1\n",
      "tensor([0.67253], device='cuda:0', grad_fn=<AddBackward0>) tensor(8939.58105, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.749      0.721      0.773      0.518\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      33/49      6.77G    0.03254    0.02917    0.01002         36        640: 1\n",
      "tensor([0.85651], device='cuda:0', grad_fn=<AddBackward0>) tensor(10148.42090, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.745      0.724      0.771      0.515\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      34/49      6.77G    0.03247    0.02923   0.009782         44        640: 1\n",
      "tensor([0.86534], device='cuda:0', grad_fn=<AddBackward0>) tensor(11233.71973, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.742      0.728      0.775      0.517\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      35/49      6.77G    0.03199    0.02892   0.009623         34        640: 1\n",
      "tensor([0.70582], device='cuda:0', grad_fn=<AddBackward0>) tensor(10418.53320, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.753      0.723      0.776       0.52\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      36/49      6.77G    0.03189    0.02883   0.009395         56        640: 1\n",
      "tensor([0.79652], device='cuda:0', grad_fn=<AddBackward0>) tensor(9361.44629, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.747      0.724      0.777      0.524\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      37/49      6.77G    0.03164    0.02848   0.009266         44        640: 1\n",
      "tensor([0.74066], device='cuda:0', grad_fn=<AddBackward0>) tensor(8747.50195, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.723      0.779      0.526\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      38/49      6.77G    0.03137    0.02817   0.009231         34        640: 1\n",
      "tensor([0.71650], device='cuda:0', grad_fn=<AddBackward0>) tensor(9313.37988, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.752      0.729       0.78      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      39/49      6.77G    0.03124    0.02815   0.008996         48        640: 1\n",
      "tensor([0.83716], device='cuda:0', grad_fn=<AddBackward0>) tensor(9902.91016, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.724       0.78      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      40/49      6.77G    0.03091    0.02827   0.008768         56        640: 1\n",
      "tensor([0.86003], device='cuda:0', grad_fn=<AddBackward0>) tensor(11626.04785, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032       0.75      0.724      0.779      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      41/49      6.77G    0.03067    0.02798   0.008664         36        640: 1\n",
      "tensor([0.68235], device='cuda:0', grad_fn=<AddBackward0>) tensor(9418.55859, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.749      0.732       0.78      0.527\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      42/49      6.77G     0.0307     0.0279   0.008599         41        640: 1\n",
      "tensor([0.86215], device='cuda:0', grad_fn=<AddBackward0>) tensor(9889.38672, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.749      0.729       0.78      0.528\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      43/49      6.77G    0.03022    0.02783   0.008418         38        640: 1\n",
      "tensor([0.67130], device='cuda:0', grad_fn=<AddBackward0>) tensor(9156.46777, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.754      0.728      0.781      0.528\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      44/49      6.77G    0.02998    0.02734   0.008126         21        640: 1\n",
      "tensor([0.59120], device='cuda:0', grad_fn=<AddBackward0>) tensor(9565.90430, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.725       0.78      0.528\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      45/49      6.77G     0.0298    0.02721   0.008106         40        640: 1\n",
      "tensor([0.72759], device='cuda:0', grad_fn=<AddBackward0>) tensor(10979.53711, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.754      0.725       0.78      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      46/49      6.77G    0.02944    0.02696   0.007888         40        640: 1\n",
      "tensor([0.77533], device='cuda:0', grad_fn=<AddBackward0>) tensor(9545.61230, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.761      0.723       0.78      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      47/49      6.77G    0.02949    0.02703   0.007735         31        640: 1\n",
      "tensor([0.63176], device='cuda:0', grad_fn=<AddBackward0>) tensor(9198.53418, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.728       0.78      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      48/49      6.77G     0.0292    0.02678   0.007618         26        640: 1\n",
      "tensor([0.59770], device='cuda:0', grad_fn=<AddBackward0>) tensor(9108.38574, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.755      0.726      0.779      0.529\n",
      "\n",
      "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
      "      49/49      6.77G    0.02891    0.02674   0.007614         30        640: 1\n",
      "tensor([0.62890], device='cuda:0', grad_fn=<AddBackward0>) tensor(8154.43994, device='cuda:0', grad_fn=<AddBackward0>) \n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.753      0.731       0.78      0.529\n",
      "\n",
      "50 epochs completed in 2.618 hours.\n",
      "Optimizer stripped from runs/train/replay_POD_Lwf_1_3_5_7_9_13_17_20_23/weights/last.pt, 14.5MB\n",
      "Optimizer stripped from runs/train/replay_POD_Lwf_1_3_5_7_9_13_17_20_23/weights/best.pt, 14.5MB\n",
      "\n",
      "Validating runs/train/replay_POD_Lwf_1_3_5_7_9_13_17_20_23/weights/best.pt...\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.762      0.723       0.78      0.529\n",
      "                   car       4952       1201      0.816      0.889      0.916       0.69\n",
      "                person       4952       4528      0.837      0.805      0.871      0.568\n",
      "             aeroplane       4952        285      0.902      0.789       0.87      0.562\n",
      "               bicycle       4952        337      0.856      0.777      0.864       0.59\n",
      "                  bird       4952        459      0.732      0.656      0.712      0.453\n",
      "                  boat       4952        263      0.604      0.608      0.645      0.374\n",
      "                bottle       4952        469      0.689       0.67      0.707      0.479\n",
      "                   bus       4952        213      0.828      0.822      0.886      0.718\n",
      "                   cat       4952        358      0.821      0.732        0.8      0.547\n",
      "                 chair       4952        756      0.615      0.597      0.626      0.402\n",
      "                   cow       4952        244      0.727      0.807      0.824      0.597\n",
      "           diningtable       4952        206       0.71      0.626      0.723      0.472\n",
      "                   dog       4952        489      0.794      0.671      0.794      0.532\n",
      "                 horse       4952        348      0.864      0.839      0.886      0.615\n",
      "             motorbike       4952        325      0.818      0.778      0.864       0.55\n",
      "           pottedplant       4952        480      0.581      0.485      0.492      0.258\n",
      "                 sheep       4952        242      0.737      0.797      0.801      0.573\n",
      "                  sofa       4952        239      0.673      0.611      0.681       0.49\n",
      "                 train       4952        282      0.845      0.774      0.846      0.567\n",
      "             tvmonitor       4952        308      0.785      0.723      0.796      0.555\n",
      "Results saved to \u001b[1mruns/train/replay_POD_Lwf_1_3_5_7_9_13_17_20_23\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : replay_POD_Lwf_1_3_5_7_9_13_17_20_23\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : \u001b[38;5;39mhttps://www.comet.com/nagasaki-soyorin/yolov5/3cd2373a500446f5b236cb06b17813ae\u001b[0m\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_f1                : 0.8419045605002714\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_false_positives   : 25.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5            : 0.8704602549080372\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_mAP@.5:.95        : 0.5622027845363232\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_precision         : 0.9017949676584215\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_recall            : 0.7894736842105263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_support           : 285\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     aeroplane_true_positives    : 225.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_f1                  : 0.8150597529875161\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_false_positives     : 44.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5              : 0.8642390602094154\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_mAP@.5:.95          : 0.5899076144958837\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_precision           : 0.8564956278524996\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_recall              : 0.7774480712166172\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_support             : 337\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bicycle_true_positives      : 262.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_f1                     : 0.6922728885359092\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_false_positives        : 110.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5                 : 0.7115796818030322\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_mAP@.5:.95             : 0.45277581516533677\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_precision              : 0.7324979527989001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_recall                 : 0.6562357564536214\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_support                : 459\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bird_true_positives         : 301.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_f1                     : 0.6061507457535535\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_false_positives        : 105.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5                 : 0.6453901591427172\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_mAP@.5:.95             : 0.37370603003712044\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_precision              : 0.6039525326737066\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_recall                 : 0.6083650190114068\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_support                : 263\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     boat_true_positives         : 160.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_f1                   : 0.6793113117796297\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_false_positives      : 141.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5               : 0.7073858427092669\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_mAP@.5:.95           : 0.4786957503198354\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_precision            : 0.6894042898766246\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_recall               : 0.6695095948827292\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_support              : 469\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bottle_true_positives       : 314.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_f1                      : 0.8246660755339855\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_false_positives         : 36.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5                  : 0.8856801596680833\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_mAP@.5:.95              : 0.7184391249351961\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_precision               : 0.8277589333525076\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_recall                  : 0.8215962441314554\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_support                 : 213\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bus_true_positives          : 175.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_f1                      : 0.8509378245082511\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_false_positives         : 241.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5                  : 0.9155631421984615\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_mAP@.5:.95              : 0.6901440228700628\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_precision               : 0.8157830204080765\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_recall                  : 0.8892589508742714\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_support                 : 1201\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     car_true_positives          : 1068.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_f1                      : 0.774323007972775\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_false_positives         : 57.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5                  : 0.8004779203614634\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_mAP@.5:.95              : 0.5465270681288206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_precision               : 0.8214153612860509\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_recall                  : 0.7323375517044045\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_support                 : 358\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cat_true_positives          : 262.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_f1                    : 0.6056360940217158\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_false_positives       : 282.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5                : 0.6261647749146639\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_mAP@.5:.95            : 0.4016168106749231\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_precision             : 0.6149917232199493\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_recall                : 0.5965608465608465\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_support               : 756\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     chair_true_positives        : 451.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_f1                      : 0.7648162388155432\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_false_positives         : 74.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5                  : 0.8241842244914441\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_mAP@.5:.95              : 0.5965382208640609\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_precision               : 0.7265179108394747\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_recall                  : 0.8073770491803278\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_support                 : 244\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cow_true_positives          : 197.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_f1              : 0.6655106412887328\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_false_positives : 53.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5          : 0.7227697520351039\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_mAP@.5:.95      : 0.47165794908157405\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_precision       : 0.7100699651344219\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_recall          : 0.6262135922330098\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_support         : 206\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     diningtable_true_positives  : 129.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_f1                      : 0.7270654327210607\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_false_positives         : 85.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5                  : 0.7940795919952126\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_mAP@.5:.95              : 0.532176378686737\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_precision               : 0.7936946374267262\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_recall                  : 0.6707566462167689\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_support                 : 489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     dog_true_positives          : 328.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_f1                    : 0.8512125422578996\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_false_positives       : 46.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5                : 0.886303613046465\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_mAP@.5:.95            : 0.6152268076817766\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_precision             : 0.8637006020461634\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_recall                : 0.8390804597701149\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_support               : 348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     horse_true_positives        : 292.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [5815]                 : (0.7259030938148499, 4.2129034996032715)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5 [100]       : (0.11504204192722196, 0.7807447086279103)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/mAP_0.5:0.95 [100]  : (0.056412308575994816, 0.5293948403796866)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/precision [100]     : (0.3624689332559514, 0.7606210008057552)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     metrics/recall [100]        : (0.1379533489489671, 0.7317622860890294)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_f1                : 0.797912197208034\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_false_positives   : 56.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5            : 0.8641344773255963\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_mAP@.5:.95        : 0.5495047413023502\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_precision         : 0.8183597537218306\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_recall            : 0.7784615384615384\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_support           : 325\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     motorbike_true_positives    : 253.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_f1                   : 0.820792774170477\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_false_positives      : 712.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5               : 0.871238961848079\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_mAP@.5:.95           : 0.5678906179061448\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_precision            : 0.8367499142819489\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_recall               : 0.8054328621908127\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_support              : 4528\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     person_true_positives       : 3647.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_f1              : 0.5290416126186374\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_false_positives : 168.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5          : 0.49232674900630286\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_mAP@.5:.95      : 0.2576341011703268\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_precision       : 0.5812820930864364\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_recall          : 0.48541666666666666\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_support         : 480\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     pottedplant_true_positives  : 233.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_f1                    : 0.7655493954498445\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_false_positives       : 69.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5                : 0.8013471883006561\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_mAP@.5:.95            : 0.5730359921790704\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_precision             : 0.7365079981683348\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_recall                : 0.7969750826893685\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_support               : 242\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sheep_true_positives        : 193.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_f1                     : 0.6404139482470796\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_false_positives        : 71.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5                 : 0.6805983908864132\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_mAP@.5:.95             : 0.4900368850508703\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_precision              : 0.6729503298599276\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_recall                 : 0.6108786610878661\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_support                : 239\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sofa_true_positives         : 146.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/box_loss [100]        : (0.02891130931675434, 0.06686333566904068)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/cls_loss [100]        : (0.0076136463321745396, 0.05841079726815224)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/obj_loss [100]        : (0.02674075774848461, 0.045597728341817856)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1                    : 0.8079759163815786\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_false_positives       : 40.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5                : 0.8457470136942313\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_mAP@.5:.95            : 0.5670191514006114\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_precision             : 0.8451154605564829\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_recall                : 0.7739632362859313\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_support               : 282\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_true_positives        : 218.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_f1                : 0.7527525029055107\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_false_positives   : 61.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5            : 0.79631076190005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_mAP@.5:.95        : 0.5547057160469998\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_precision         : 0.7849858935479549\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_recall            : 0.7230618605618605\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_support           : 308\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     tvmonitor_true_positives    : 223.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/box_loss [100]          : (0.031235525384545326, 0.04720846191048622)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/cls_loss [100]          : (0.008146350271999836, 0.04693417251110077)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val/obj_loss [100]          : (0.01827402412891388, 0.02381983771920204)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr0 [100]                 : (0.0004960000000000005, 0.07002579535683577)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr1 [100]                 : (0.0004960000000000005, 0.009601247348810548)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     x/lr2 [100]                 : (0.0004960000000000005, 0.009601247348810548)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name                        : replay_POD_Lwf_1_3_5_7_9_13_17_20_23\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Run Path                    : nagasaki-soyorin/yolov5/3cd2373a500446f5b236cb06b17813ae\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_batch_metrics     : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_confusion_matrix  : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_log_per_class_metrics : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_max_image_uploads     : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_mode                  : online\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     comet_model_name            : yolov5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams             : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_enable          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DER_old_model       : []\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Distillation_layers : [1, 3, 5, 7, 9, 13, 17, 20, 23]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_enable          : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_lambda          : [1e-05]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Lwf_temperature     : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Old_models          : ['./runs/train/fog_02/weights/last.pt']\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     PODNet_enable       : True\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     POD_lambda          : 100.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_enable           : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_lambda           : 10.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     SI_pt               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     anchor_t            : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     artifact_alias      : latest\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     batch_size          : 16\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bbox_interval       : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     box                 : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     bucket              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cache               : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cfg                 : models/yolov5s_VOCKITTI.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls                 : 0.1625\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cls_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     copy_paste          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     cos_lr              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     data                : data/VOCKITTIBiC_base.yaml\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     degrees             : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     device              : \n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     entity              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epochs              : 50\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     evolve_population   : data/hyps\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_lambda          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ewc_pt              : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     exist_ok            : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fl_gamma            : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     fliplr              : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     flipud              : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     freeze              : [0]\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_h               : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_s               : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hsv_v               : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|anchor_t        : 4.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|box             : 0.05\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls             : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|cls_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|copy_paste      : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|degrees         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fl_gamma        : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|fliplr          : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|flipud          : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_h           : 0.015\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_s           : 0.7\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|hsv_v           : 0.4\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|iou_t           : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lr0             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|lrf             : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mixup           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|momentum        : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|mosaic          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj             : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|obj_pw          : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|perspective     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|scale           : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|shear           : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|translate       : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_bias_lr  : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_epochs   : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|warmup_momentum : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hyp|weight_decay    : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     image_weights       : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     imgsz               : 640\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     iou_t               : 0.2\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     label_smoothing     : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     local_rank          : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lr0                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     lrf                 : 0.01\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mixup               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     momentum            : 0.937\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     mosaic              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     multi_scale         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                : replay_POD_Lwf_1_3_5_7_9_13_17_20_23\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_console      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ndjson_file         : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noautoanchor        : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noplots             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     nosave              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     noval               : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj                 : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     obj_pw              : 1.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     optimizer           : SGD\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     patience            : 100\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     perspective         : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     project             : runs/train\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     quad                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     rect                : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume              : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     resume_evolve       : None\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_dir            : runs/train/replay_POD_Lwf_1_3_5_7_9_13_17_20_23\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     save_period         : -1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     scale               : 0.5\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     seed                : 0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     shear               : 0.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     single_cls          : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     sync_bn             : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     translate           : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     upload_dataset      : False\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_conf_threshold  : 0.001\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_iou_threshold   : 0.6\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_bias_lr      : 0.1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_epochs       : 3.0\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     warmup_momentum     : 0.8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weight_decay        : 0.0005\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     weights             : ./runs/train/fog_02/weights/last.pt\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     workers             : 8\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset                        : 13 (3.06 MB)\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-environment-definition : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-info                   : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     conda-specification          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     confusion-matrix             : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details          : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     git metadata                 : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     images                       : 106\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages           : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages                  : 1\n",
      "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n"
     ]
    }
   ],
   "source": [
    "pod_layers = '1 3 5 7 9 13 17 20 23'\n",
    "pod_name = pod_layers.replace(' ', '_')\n",
    "command = f\"\"\"\n",
    "env COMET_LOG_PER_CLASS_METRICS=true python train_PODNet.py \\\n",
    "--img 640 \\\n",
    "--bbox_interval 1 \\\n",
    "--cfg models/yolov5s_VOCKITTI.yaml \\\n",
    "--data data/VOCKITTIBiC_base.yaml \\\n",
    "--epochs 50 \\\n",
    "--weights ./runs/train/fog_02/weights/last.pt \\\n",
    "--Lwf_enable \\\n",
    "--Lwf_temperature 1.0 \\\n",
    "--Lwf_lambda 1e-5 \\\n",
    "--PODNet_enable \\\n",
    "--Distillation_layers {pod_layers} \\\n",
    "--POD_lambda 1e2 \\\n",
    "--Old_models \\\n",
    "   ./runs/train/fog_02/weights/last.pt \\\n",
    "--name replay_POD_Lwf_{pod_name}\n",
    "\"\"\"\n",
    "!{command}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "73471158-a22e-45d0-9bcc-a29676ac517e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/VOCKITTI.yaml, weights=['runs/train/replay_POD_Lwf_1_3_5_7_9_13_17_20_23/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 b2fc1a21 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/VOC/labels/test2007.cache... 4952 image\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       4952      12032      0.753       0.73      0.779      0.529\n",
      "                   car       4952       1201      0.812      0.887      0.914       0.69\n",
      "                person       4952       4528      0.829      0.814      0.872      0.568\n",
      "             aeroplane       4952        285      0.906      0.782      0.873      0.558\n",
      "               bicycle       4952        337      0.857      0.783      0.865      0.588\n",
      "                  bird       4952        459      0.726      0.669      0.711      0.454\n",
      "                  boat       4952        263      0.599      0.612      0.639      0.373\n",
      "                bottle       4952        469       0.67      0.667      0.704      0.478\n",
      "                   bus       4952        213      0.825      0.816      0.885      0.722\n",
      "                   cat       4952        358      0.809      0.737      0.795      0.543\n",
      "                 chair       4952        756      0.604      0.602      0.623      0.401\n",
      "                   cow       4952        244      0.725      0.824      0.826      0.597\n",
      "           diningtable       4952        206      0.699      0.646      0.717      0.471\n",
      "                   dog       4952        489      0.761      0.689      0.794      0.531\n",
      "                 horse       4952        348      0.856      0.845      0.886       0.62\n",
      "             motorbike       4952        325      0.819       0.78      0.862      0.548\n",
      "           pottedplant       4952        480      0.569      0.494      0.489      0.257\n",
      "                 sheep       4952        242      0.718      0.798      0.801      0.568\n",
      "                  sofa       4952        239      0.667       0.64      0.681      0.491\n",
      "                 train       4952        282      0.844      0.787      0.852      0.565\n",
      "             tvmonitor       4952        308      0.771      0.727      0.792      0.554\n",
      "Speed: 0.1ms pre-process, 1.5ms inference, 0.7ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp330\u001b[0m\n",
      "VOC val successful|ly!\n",
      "\u001b[34m\u001b[1mval: \u001b[0mdata=data/kitti_increment.yaml, weights=['runs/train/replay_POD_Lwf_1_3_5_7_9_13_17_20_23/weights/last.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=test, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val, name=exp, exist_ok=False, half=False, dnn=False\n",
      "YOLOv5 🚀 b2fc1a21 Python-3.10.8 torch-2.1.2+cu118 CUDA:0 (NVIDIA GeForce RTX 3090, 24135MiB)\n",
      "\n",
      "Fusing layers... \n",
      "YOLOv5s_VOCKITTI summary: 160 layers, 7080253 parameters, 0 gradients, 16.0 GFLOPs\n",
      "\u001b[34m\u001b[1mtest: \u001b[0mScanning /root/autodl-tmp/datasets/kitti/labels/test.cache... 2244 images,\u001b[0m\n",
      "                 Class     Images  Instances          P          R      mAP50   \n",
      "                   all       2244      12198      0.868      0.791      0.862      0.567\n",
      "                   car       2244       8711      0.922      0.889      0.951      0.722\n",
      "                   van       2244        861      0.907      0.856      0.915      0.671\n",
      "                 truck       2244        333      0.935      0.958      0.973       0.75\n",
      "                  tram       2244        138      0.892      0.901      0.954      0.628\n",
      "                person       2244       1286      0.881      0.667      0.776      0.412\n",
      "        person_sitting       2244         89      0.608      0.539      0.603      0.317\n",
      "               cyclist       2244        496      0.877      0.734      0.848      0.481\n",
      "                  misc       2244        284      0.921      0.782      0.875      0.557\n",
      "Speed: 0.0ms pre-process, 1.0ms inference, 0.8ms NMS per image at shape (32, 3, 640, 640)\n",
      "Results saved to \u001b[1mruns/val/exp331\u001b[0m\n",
      "KITTI val successfully!\n"
     ]
    }
   ],
   "source": [
    "# 这是无雾训练集\n",
    "model = f'runs/train/replay_POD_Lwf_{pod_name}/weights/last.pt'\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/VOCKITTI.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'VOC val successful|ly!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "\n",
    "\n",
    "val_command = f\" \\\n",
    "python val.py \\\n",
    "--data data/kitti_increment.yaml \\\n",
    "--weights {model} \\\n",
    "--task test &&\\\n",
    "echo 'KITTI val successfully!' \\\n",
    "\" \n",
    "!{val_command}\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c7cf9df-ee03-472e-9f2a-e2567499b9fc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
